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In recent years, natural disasters occur frequently, and secondary disasters induced by major disasters will also cause huge losses. The diversity of secondary disasters makes humanitarian emergency logistics (HEL) more challenging but often overlooked by researchers. In order to solve the comprehensive HEL problem of major and secondary disasters, a three-stage mixed integer linear optimization (TS-MILO) model is proposed. Among them, the uncertainty of the demand for relief supplies is also extremely difficult to deal with. In order to resist the interference of uncertainty, based on robust optimization, the TS-MILO model is further transformed into a three-stage mixed integer robust optimization (TS-MIRO) model, which are respectively BTS-MIRO (Box set), PTS-MIRO (Polyhedral set), and ETS-MIRO (Ellipsoid set). The experimental results show that the TS-MILO model can provide the lowest cost but cannot solve the uncertainty problem. The improved TS-MIRO model will pay a robust price (increase by at least 10.05%), but maintains supply stability even in the worst-case scenario. Specifically, ETS-MIRO model has strong robustness, and its cost increase only accounts for 44.66% of BTS-MIRO model in the worst case. The service level of the three TS-MIRO models increases with the safety parameters, among which the service level in the ETS-MIRO model increases significantly from 88.53% to 96.44%. The research results can provide a strong support for the decision making of disaster relief management department.

Children’s exposure to violence has become a severe problem with the rapid development of Internet. Recognizing violent video and estimating violence extent become crucial. Most researches focus on violent scene or violent action detection, lacking overall violence extent information. In this paper, we propose a violence rating prediction approach and build a novel violent video dataset. Our proposed method has two advantages: (1) videos are represented by features extracted from a learned two-stream network; (2) relationship between different violence extent can be learned and utilized to predict violence rating. To demonstrate the effectiveness of our method, we created a well-labelled dataset which contains 1, 930 violent videos. Each video is labelled with 6 objective violent attributes. Furthermore, we employ pairwise comparison method to obtain ground-truth violence rating for each video. Our proposed approach was evaluated on our dataset. Its results showed that our proposed method outperforms the state-of-art video classification methods.

A label-free real-time detection method of neuronal firing is studied in this paper. The relation between the optical phase variation and membrane potential of a neuron model is investigated. The nature of action potential is the transmembrane flow of ions, e.g. Na+ and K+, on both sides of the cell membrane. That is, the ion concentration in the cell body has large change during the generation of action potential. The variation of ion concentration will further affect the refractive index and the phase distribution of a sample is affected by the related refractive index distribution. Therefore, the variation of phase is actually induced by the variation of ion concentration in the cell body. In other words, the neuron firing can be described via optical phase information. To explore the correlation between the neuronal firing activities and the variation of optical phase, a phase imaging experiment is designed and implemented. Firstly, the phase distribution of the simulation model in the solution with different concentration is obtained. Then, the phase value of sampling point in the solution of different concentration is obtained and the phase has linear relation with Na+ ion concentration by fitting analysis. Finally, the quantity associated with Na+ ion concentration in the neuronal model is replaced with the phase value. The experimental result and the subsequent simulation agree well with the theoretical analysis. This work could give a basis to extract and express the real-time information of membrane potential visually with label-free approaches.

This paper focuses on economical operation of a grid-connected microgrid in real-time. An online optimization model is developed to achieve the microgrid operation in the process of dynamic optimal control, and through information feedback and online optimization to reduce the impact of uncertainty in a MG. The online optimization model has the characteristics of nonlinear and discontinuous, so an approximate dynamic programming (ADP) algorithm is used to solve this online model. Finally, the proposed online optimization model is tested in the European benchmark microgrid system. The simulation results show that the online optimization model overcomes the impacts of random fluctuations of renewable energy and loads and reduces the operating cost of the microgrid.

The Cyber-physical system (CPS) will soon become the basic technology of contemporary industry, which consists of two interacting networks where the cyber network overlays the physical network. Due to the high interdependence, the CPS are more fragile in general when facing attacks. In order to avoid the occurrence of catastrophic accidents while taking into account the cost of construction, it is critical to find and protect key nodes whose failure will lead to serious cascading failures. In this paper, we present a way to evaluate the node importance in CPS based on information theory, where node states are treated as discrete random variables, and their importance is interpreted as the information gain. After research, we find that self-information of a node state can reflect its ability to influence the whole system. In particular, the result of this method is affected by the probability of attacking nodes, which helps to assess the node importance in different scenarios and environments.

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.

In this paper, a new direction-of-arrival (DOA) estimation method is proposed based on the array cross-correlation vector (ACCV) model which can decrease the computational complexity of multiple measurement vectors (MMV) model. Firstly, the ACCV model is refined to accommodate the correlated signal scenario. Then by properly incorporating the folding scheme in the compressive sensing (CS) framework, a new algorithm termed folding orthogona matching pursuit (FOMP) is proposed in the reconstruction of CS framework. The method has a lower computational complexity and higher performance compared with other existing DOA algorithms. Numerical results are presented to verify the efficiency of the proposed method.

For fast acquisition of GPS pseudo-noise (PN) code, folding is often used to speed up the search process. By properly incorporating the folding in the compressive sensing (CS) framework, a new low-complexity GPS signal acquisition scheme is proposed. In the CS reconstruction step, a new method is proposed termed projection elimination (PE) algorithm, which has lower computational complexity and nearly the same performance compared to the orthogonal matching pursuit (OMP) algorithm for very sparse signals. Simulation results are given to demonstrate its effectiveness.

Simulation of Ultrasound (US) images from other image modalities like MRI (Magnetic Resonance Imaging) is playing increasingly important role on education training, image registration applications and minimally invasive procedures. However, the computation speed and implementation is still a great challenge in clinical application. In this paper, we propose a framework of accurate simulation of freehand US images from MRI 3D volumes in real time. The proposed method takes physical phenomena into account by means of the ray-based model, enabling accurate simulation of speckle pattern without any preprocessing. First, we constructed tissue models from MRI scanned information using segmentation and ray-based methods. Second, simulated US image was generated combining reflection, transmission, absorption and speckle pattern. The simulated result is promising with the computation time less than 1s.

Aim at the problem of road transportation security, this paper uses a clustering method to deal with the vehicle data collected by GPS equipment. From the GPS real-time supervisory data, we could explore the locations with overspeed density higher than the threshold and create the overspeed points cluster at maximum density value. Those sections with density higher than this maximum value are overspeed events multiple sections. The application of adjacency list replaced R*- Tree simplifies the establishment of data structure and reduces the memory footprint. Then use the clustering result to get the related information of the key monitoring vehicles and driver. The experiment results demonstrate the validity of our approach. The comprehensive research results enable department of transportation management and transportation enterprises to achieve safe supervision.

The method of normalized digital orthogonal detection is proposed according to the frequency of surface Magnetic Resonance Sounding (MRS) signal is much bigger than bandwidth and it has no influence on its key parameters. Constructing MRS signal with noise in accordance with its characteristics, then processing them using normalized digital orthogonal detection, the simulation results show that with the same SNR, the higher the sampling frequency, the bigger SNIR (signal noise improvement ratio). Embedding the normalized digital orthogonal detection unit in MRS instrument, and conducting laboratory test and field detection, the results show that this method is effective to extract the envelope of MRS signal, and SNR can be improved, too. At the same time, it simplifies hardware and improves the stability of orthogonal detection system.

Incident management system is one of the important part of ATIS, the prediction of incident duration is an important foundation of expressway incident management system. The marked influence factors of incident duration are determined by analysis of variance, then stepwise regression analysis and decision tree are used to predict the incident duration of expressway. In order to reduce errors of the prediction based on stepwise regression analysis and decision tree, Put forward the Multi-model fusion algorithm.

Experiments were carried out to study the process of sulphur dioxide capture with cement raw meals, and effects of sulphur evaporation (i.e. the captured sulphur in the raw meal evaporates into SO 2 at a calcination temperature) on the efficiency such a process. In the study, four samples of raw meals with various ingredients were prepared; with a combined application of DSC-TGA-FTIR, the amount /degree of SO 2 released from those samples under normal atmosphere were measured /analysed based on measurements results with TG, DSC, DTG and IR 3D imaging / mapping. Results as obtained showed that: 1. the amount of released SO 2 increased with an increase of sulphur content in raw meals when temperature increases from 35 °C to 1450 °C; 2. a higher temperature is required to initiate SO 2 releases when the sulphur content in raw meals is lower. Such results are in an agreement with a fact that a decrease sulphur content in a raw meal leads to a significant increase its melting and calcinating temperature, and preventing the formation of liquidus, and sulphur from evaporating.

With the rapid development of the integration of the world economy, the two deltas, that is, the Yangtze River Delta and the Pearl River Delta have developed fastest and are the strongest and the most dynamic areas. The economic development of the two areas plays a most important role in the all-around development of China's economy. The paper introduces factor analysis and clustering analysis of multivariate statistical analysis to appraise the development status of the above two regions' economy, finds out the reason why the economic development is different in different clusters.

Until now there is no unified index system for agricultural production modernization around the country. This research takes the agricultural production modernization as a system. Based on the full consideration on the status quo, the index optimization has been conducted gradually by small-group discussions, specific research, counseling with the peers and administrative department. Combined with the availability and quality of statistical data, the index adjustment has been done with the aid of modern statistical analysis methods, so a systematic index system of agricultural production modernization has been formed. Then, taking 13 cities of Jiangsu Province as the example, data of the agricultural production modernization have been collected, standardized and analyzed relevantly in accordance with the hierarchy analysis and statistical model. Finally, the process of the agricultural production modernization in Jiangsu province has been appraised and sorted.

Kalman filter is used commonly in agriculture vehicle navigation, but it is limited in linear system and has special request on noises. In practice, it is difficult to meet all the requests. To avoid the disadvantage of kalman filter, the RBF neural network is used to fuse the multi-sensor information to get the position information; Particle Swarm Optimization theory is introduced to the learning RBF neural network training process. The best neural network structure can be found by using particle swarm optimization algorithm to optimize the parameters of the RBF neural network. Experiment results indicate that RBF algorithm is better than the kalman filter, which can obtain more precise and more robust position information.

The blood-brain barrier (BBB) is a major obstacle for drug delivery to the brain for treatment of CNS disorders. To search for more convenient models in studying the transport across the BBB, we compared four in vitro models: endothelial monoculture (bEnd3 cell line), coculture of bEnd3 and astrocytes (coculture), coculture with collagen type I and IV mixture, and coculture with Matrigel. We also quantified the hydraulic conductivity (Lp), trans-electrical resistance (TER) and diffusive permeability (P) of these models to three solutes: TAMRA, Dextran 10K and Dextran70K. Our results showed that Lp and P of the endothelial monolayer and coculture models are not different from each other. Compared with in vivo permeability data from rat pial microvessels [1], P of the endothelial monolayer and coculture models are not significantly different from in vivo data for Dextran 70K while they are 2–4 times higher for TAMRA and Dextran 10K. The results suggested that endothelial monolayer and all the coculture models are fairly good models for studying transport of relatively large solutes across the BBB.

The subject investigated of this paper is major in the ECT system of 8-electrode oil-water two-phase flow, and the measuring principle is analyzed, the mathematic model of the oil-water two-phase distribution information is established based on double integral infinitesimal principle, an ECT measurement system of 8-electrode oil-water two-phase flow is set up. At the same time we present the method of image reconstruction to accomplish the concentration measurement of oil-water two-phase flow. Based on ART image-reconstruction algorithm, we present threshold value to improve quality of image reconstruction and calculating accuracy of content and the precision of reconstruction is about 1%, which can meet the need of identification of flow regime for oil-water two-phase flow.

Despite the progresses in developing pulsatile impeller pump and impeller total heart, as well as in applying streamlined impeller vanes, the best results in application of artificial heart pumps have been achieved by nonpulsatile univentricular assist pump with straight impeller vanes until now. It seems all efforts and successes have been done in vain because artificial heart rejects HiTech! This paper recalls some important achievements in R&D of artificial heart in past 25 years and shares author's experiences with the readers.

In this paper, we propose a novel scalable FFT processor design capable of handling various transform sizes and data channels for multi-standard MIMO processing. The basic building blocks are split-radix 2/4/8 FFT modules in a modified MDC (multi-path delay commutator) form, pre- ordering data buffer, radix-2 butterfly module and twiddle factor module. Via proper data flow reconfiguration plus two alternative working frequencies, the processor can support 64, 128, 512, 1024-point transforms plus 1 to 4 MIMO channels. The hardware utilization is kept as 100% under various working modes. The implementation results in TSMC 0.18 um process indicate the design has a core size of about 5.5 mm2, and a power consumption of 320 mW at 100 MHz. The delivered performance qualifies the proposed design for 802.1 In and 802.16e applications.

This paper proposes a novel approach to robust principal component analysis (PCA). It first searches for a subset of most reliable inliers from original data by projection pursuit. We define an index function for each projection direction and utilize a nonparametric mode search technique, the mean shift, to obtain the index value. The inlier subset is identified from data projections on the direction with highest index. We discover the initial principal subspace from the inlier subset, and project all the data onto that subspace. The outliers are then detected based on the analysis of the squared prediction error (SPE) of each sample, which measures the distance between the sample and its projection on that subspace. Experimental results on both synthetic data and a real image set illustrate the effectiveness of our approach in removing outliers and obtaining the reliable PCA solution.

This paper introduces a new kind of instrument designed to inspect the dynamic characteristics of the automobile. It mainly introduces the principle and the design of the circuit of the instrument. This instrument is used to inspect dynamic characteristics of the accelerograph driver equipment of the automobile. The analysis of the experimental datum is provided.